Navigating Complexity Through Model-Based Systems Engineering

How does MBSE drive reliability, safety, and resilience for complex systems in critical industries?

Modern engineering systems are complex, interconnected combinations of hardware, software, and AI-enabled platforms, making them difficult to define or catalog using conventional documentation methods. Organizations relying on traditional documentation methods (spreadsheets, static text documents, CAD/PowerPoint diagrams) can benefit from a more structured approach to maintain speed to market and anticipate failures  — whether those failures involve software errors, hardware malfunctions, or integration issues. And when systems fail, structured frameworks can provide a clearer record of how decisions evolved — better supporting failure and root-cause analysis.

Model-based systems engineering (MBSE) consolidates system data into a unified digital model. Adopting industries may encounter hurdles to implementing MBSE, but for organizations managing complex systems, the question isn't whether current systems justify the investment, but whether future systems — more autonomous, more interconnected, more regulated — can be governed without MBSE. Success with MBSE will benefit from specialized expertise as leverage to architect and evaluate complex — and evolving — system and product requirements. By employing multidisciplinary engineering insights to understand the behavior, structure, and function of actual products, materials, and processes, system engineers can develop more reliable models that meaningfully reduce risk in high-consequence, real-world applications.

What is MBSE and how is it implemented?

An MBSE approach changes how engineering teams work by replacing disconnected documents with a single, connected model. In contrast to static text files, drawings, and spreadsheets that capture only snapshots of information, an MBSE model is a dynamic, queryable environment that reflects stakeholders' formal goals and needs, behavioral logic, use cases, physical architecture and interfaces, requirements and constraints, relevant material and performance properties, and the relationships between all these elements.

An MBSE approach will often involve:

• Defining clear system boundaries — what is inside the "system of interest" and what is outside
• Deriving and organizing use cases for each stakeholder profile
• Identifying system functions based on the use cases
• Defining the system architecture that performs the objective functions
• Diagramming connections within the system
• Allocating these functions to specific architecture components, making responsibilities and hand‑offs unambiguous through the logical flow of the system
• Diagramming different system states and how specific signals invoke state changes

Because the architecture, behavior, and requirements are all visible and traceable in one model, teams can make clearer design decisions and define robust, testable requirements before prototyping while staying aligned with customer needs. The explicit links that connect goals, requirements, design elements, analyses, and tests form a digital thread, which lets engineers see the impact of change quickly and maintain agility in a dynamic environment.

How does MBSE help stakeholders address product complexity?

Importantly, leveraging MBSE enables mechanical, electrical, software, materials, and other engineers to work within the same environment as systems engineers so that decisions such as mechanical constraints (i.e., material durability or strength) are addressed in the concept stage, not discovered during testing in the development stage. By promoting multidisciplinary collaboration, MBSE models help design teams and decision-makers: 

• Understand how changes propagate across hardware, software, and interfaces
• Evaluate design tradeoffs earlier, before costly commitments
• Maintain traceability from initial requirements through implementation and validation
• Identify gaps, conflicts, or unintended consequences that may not be visible in siloed documentation
• Surface cybersecurity risks and dependencies earlier — enabling security requirements to be integrated into the system architecture from the outset, rather than retrofitted after design commitments have been made
• Accurately and rapidly perform failure analysis when something goes wrong in a product or deployed system
• Navigate disputes with definitive, traceable, queryable records of system design decisions and component relationships and dynamics

One of the most consequential advantages of MBSE is validating system interactions and exposing integration risks in the digital domain before hardware is built or software is deployed. For example, the MBSE model may reveal that two robotic vehicle payloads, assumed to be plug-and-play because they share a standard connector, actually operate with different power and data conventions, which could cause the system to become inoperable or even damaged in certain states. Identifying this incompatibility in the model — rather than during system integration or operations — can avoid costly redesign and requalification.

What does MBSE look like in practice?

In one recent project, Exponent systems engineers worked with a client to develop an edge computing framework for wearable devices. The client's goal was to:

1. Collect physiological, operational, and situational data from the wearables
2. Analyze the data to support rapid, data-driven decision making
3. Visualize the data and distribute it to all participants, synchronously
4. Send and receive commands over the wearables

The technical challenges included designing a system architecture capable of processing information locally — on or near the device — rather than relying on transmission to a centralized cloud, while maintaining real-time data synchronization and operational reliability across multiple devices in diverse environments.

The client's development process was largely ad hoc, as is common in fast-moving environments. As a result, individual components functioned, but system boundaries, interdependencies between devices, and operational responsibilities — such as which components owned specific functions, managed data flow, and were accountable for system-level behaviors — had not been formally defined, making it difficult to mature the design to the next stage of the lifecycle. As the system grew in complexity, this ambiguity became increasingly challenging; it was unclear how changes to firmware, communication protocols, or power management would affect performance, security, and reliability across the device product ecosystem.

Exponent engineers developed an MBSE model that enabled stakeholders to work from a unified, coherent system representation, eliminating the need to reconcile conflicting assumptions across separate documents. As the team decomposed and defined the system, they established traceability at every stage of the MBSE workflow — linking stakeholder needs, use cases, and both functional and logical architectures directly to the evolving requirements. Through the model's traceability matrices, Exponent engineers were able to identify gaps and inconsistencies in the initial design.

As requirements changed, the MBSE model provided a reliable foundation for adapting the system without losing track of prior decisions, because relationships could be queried directly in the model. Design decisions regarding architecture and behavior could be evaluated in their proper context, making their downstream effects and implications visible and traceable throughout the system lifecycle.

What benefits do MBSE models offer? 

Creating continuity across the system lifecycle

MBSE carries understanding forward as projects move from concept through development, validation, and maintenance. In fast-moving, innovative development environments, urgency to build prototypes and show progress can result in documentation being deprioritized — yet this is a critical aspect of detecting complex design issues early, particularly those triggered by cascading changes across integrated systems with numerous hardware and software interfaces. Rather than reconstructing intent at each stage or losing institutional knowledge when teams expand or change, organizations can use MBSE models to build a consistent foundation that scales. For programs spanning years or decades — common in defense and infrastructure projects — this continuity is not a convenience; it is often a business requirement.

When system representations are grounded in how products actually behave, the resulting requirements are more robust, and the gap between design intent and real-world performance narrows.

Deeper scientific integration across disciplines

MBSE provides a structured environment for embedding domain knowledge rigorously across disciplines. By capturing physical properties, material behavior, environmental conditions, and failure mechanisms directly within the model, MBSE creates explicit, traceable connections between fields that are often siloed in practice — thermal science, materials science, mechanical and electrical engineering, metallurgical analysis, software systems, and more. Design decisions in one domain become visible to engineers working in another and known failure mechanisms can be encoded as constraints rather than discovered late in testing. When system representations are grounded in how products actually behave, the resulting requirements are more robust, and the gap between design intent and real-world performance narrows. 

Stronger footing for failure analysis 

When a system fails, the ability to reconstruct what happened depends heavily on the quality of the record left behind. A well-maintained MBSE model provides exactly that: a traceable, queryable account of system design, requirements, and behavioral logic that investigators can examine directly — tracing how requirements were defined, how decisions were made, and where system behavior diverged from its intended specification. This makes MBSE a significant asset not just during development, but in post-failure contexts including regulatory review, liability assessment, and litigation. 

How can stakeholders build tomorrow's intelligent — and trusted — systems?

AI is enabling organizations to automatically generate and refine system models, accelerating design cycles and uncovering insights that previously required manual engineering effort. As digital twins grow more sophisticated — converging real-time IoT data, AI, and expanded computing power — they are evolving from reactive monitoring tools into AI-powered systems capable of predicting, simulating, and in some cases autonomously optimizing operations. As automation and AI drive system complexity, MBSE models can serve as the critical, traceable framework necessary to build trust in safety-critical systems across industries.